Plotting extravaganza

The googleVis package in R

#Install the package
#install.packages("googlevis")
#Load the package
suppressMessages(library(googleVis))

(The simplest) Scatter Chart

Scatter <- gvisScatterChart(women,
                            options=list(
                              legend="none",series="[{color:'pink', targetAxisIndex: 1}]",
                              lineWidth=2, pointSize=0,
                              title="Women", vAxis="{title:'weight (lbs)'}",
                              hAxis="{title:'height (in)'}",
                              width=300, height=300))
print(Scatter,'chart')

Line chart with two axis with customized lines

Dashed <-  gvisLineChart(cases, xvar="Year", yvar=c("Cases","dTap","DTwP","Switch"),options=list(
                           series="[{color:'red', targetAxisIndex: 0, 
                           lineWidth: 3, lineDashStyle: [2, 2, 20, 2, 20, 2]},
                           {color: 'aquamarine',targetAxisIndex: 1, 
                          lineWidth: 3, lineDashStyle: [4, 1]}, {color: 'darkslategrey',targetAxisIndex: 1, 
                          lineWidth: 3, lineDashStyle: [4, 1]},{color: 'cadetblue',targetAxisIndex: 1, 
                          lineWidth: 3, lineDashStyle: [4, 1]}]",
                          vAxes="[{title:'Pertussis cases Netherlands'}, {title:'Vaccine coverage %'}]", hAxes="[{title:'Year'}]", height=300))
print(Dashed, 'chart')

Timeline chart

datTL <- data.frame(Position=c("Primary","Boost","Primary","Primary","Primary","Boost","Primary", "Boost"),
                    Name=c("DwTP (3,4,5 mo)","DwTP (4 yrs)","DTwP-IPV (3,4,5, 11 mo)","DTwP-IPV-Hib (3,4,5, 11 mo)","DTwP-IPV-Hib (2,3,4, 11 mo)", "DTaP (4 yrs)", "DTaP-IPV-Hib  (2,3,4, 11 mo)", "Tdap (4 yrs)"
                           ),
                    start=as.Date(x=c("1953-01-01","1953-01-01","1962-01-01","1993-01-01", "1999-01-01","2001-01-01","2005-01-01","2006-01-01"
                                          )),
                    end=as.Date(x=c("1961-12-31","1961-12-31","1992-01-01", "1998-12-31","2005-12-31","2004-12-31","2018-02-28","2018-02-28"
                                     )))

Timeline <- gvisTimeline(data=datTL, 
                         rowlabel="Name",
                         barlabel="Position",
                         start="start", 
                         end="end",
                         options=list(timeline="{groupByRowLabel:false}",
                                      backgroundColor='azure', 
                                      height=500,width=1200,
                                      colors="['cadetblue']"))
print(Timeline,'chart')

Stepped area chart with background colours (for Toby)

SteppedArea <- gvisSteppedAreaChart(ptA, xvar="Year", 
                                    yvar=c("ptxA1", "ptxA2","ptxA4","ptxA5"),
                                    options=list(isStacked=TRUE, height=400,vAxis="{gridlines:{color:'#D3D3D3', count:3}}",backgroundColor="#D3D3D3"))
print(SteppedArea,'chart')

Area chart

Area <- gvisAreaChart(ptA, xvar="Year", 
                                    yvar=c("ptxA1", "ptxA2","ptxA4","ptxA5"),options=list(vAxis="{gridlines:{color:'white', count:3}}"))

print(Area,'chart')

Bar chart: Stacked

Bar <- gvisBarChart(ptA, xvar="Year", 
                                    yvar=c("ptxA1", "ptxA2","ptxA4","ptxA5"),options=list(isStacked=TRUE, height=700))
print(Bar,'chart')

Histogram

set.seed(123)
datHist=data.frame(A=rpois(100, 20),
                   B=rpois(100, 5),
                   C=rpois(100, 50))

Hist <- gvisHistogram(datHist, options=list(
  legend="{ position: 'top', maxLines: 2 }",
  colors="['#5C3292', '#1A8763', '#871B47']",
  width=600, height=360))
print(Hist,'chart')

Editing on the go for those pesky questions at the end of a talk

df=data.frame(country=c("US", "GB", "PT"), 
              val1=c(10,13,14), 
              val2=c(23,12,32))
Line4 <-  gvisLineChart(df, "country", c("val1","val2"),
                        options=list(gvis.editor="Play with me!",lineWidth=5 , vAxis="{gridlines:{color:'white', count:3}}"))
print(Line4,'chart')

Map

require(datasets)
states <- data.frame(state.name, state.x77)
GeoStates <- gvisGeoChart(states, "state.name", "Population",
                          options=list(region="US", 
                                       displayMode="regions", 
                                       resolution="provinces",
                                       width=600, height=400))
print(GeoStates,'chart')

Resolution and annotations

Anno <- gvisAnnotationChart(Stock, 
                            datevar="Date",
                            numvar="Value", 
                            idvar="Device",
                            titlevar="Title", 
                            annotationvar="Annotation",
                            options=list(
                              width=600, height=350,
                              fill=10, displayExactValues=TRUE,
                              colors="['#0000ff','#00ff00']")
)
print(Anno,'chart')

Calendar

Cal <- gvisCalendar(Cairo, 
                    datevar="Date", 
                    numvar="Temp",
                    options=list(
                      title="Daily temperature ",
                      height=320,
                      calendar="{yearLabel: { fontName: 'Times-Roman',
                               fontSize: 18, color: '#darkgrey', bold: true},
                               cellSize: 10,
                               cellColor: { stroke: 'red', strokeOpacity: 0.2 },
                               focusedCellColor: {stroke:'red'}}")
)
print(Cal, "chart")

Motion charts

Motion=gvisMotionChart(Fruits, 
                       idvar="Fruit", 
                       timevar="Year")
print(Motion, "chart")

The ggplotly package in R

Surfaces

# Charge the plotly library
suppressMessages(library(plotly))
suppressMessages(library(ggplot2)) 

# 3D plot 
suppressMessages(p<-plot_ly(z = volcano, type = "surface"))

p

Scatter

# Make data
a=rnorm(100)
b=sample( c(1:10) , 100 , replace=T)
 
# Make the graph
my_graph<-plot_ly(x=b , y=a , mode="markers" , size=abs(a)/2 , color=ifelse(a>0,"blue","red") ) %>% 
 
#Change hover mode in the layout argument :
layout( hovermode="closest" )
 
# show the graph
my_graph

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

Boxplot

p5 <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box")
p5

Histograms

graph=plot_ly(x = rnorm(500), opacity = 0.6, type = "histogram") %>%
    add_trace(x = rnorm(500)+1) %>%
    layout(barmode="overlay")
graph

Maps

suppressMessages(library(maps))
suppressMessages(library(dplyr))
# map data
county_df <- map_data("county")
state_df <- map_data("state")

county_df$subregion <- gsub(" ", "", county_df$subregion)

#election data
df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/votes.csv")
df <- subset(df, select = c(Obama, Romney, area_name))

df$area_name <- tolower(df$area_name) 
df$area_name <- gsub(" county", "", df$area_name)
df$area_name <- gsub(" ", "", df$area_name)
df$area_name <- gsub("[.]", "", df$area_name)

df$Obama <- df$Obama*100
df$Romney <- df$Romney*100

for (i in 1:length(df[,1])) {
  if (df$Obama[i] > df$Romney[i]) {
    df$Percent[i] = df$Obama[i]
  } else {
    df$Percent[i] = -df$Romney[i]
  }
}

names(df) <- c("Obama", "Romney", "subregion", "Percent")

# join data
US <- inner_join(county_df, df, by = "subregion")
US <- US[!duplicated(US$order), ]

# colorramp
blue <- colorRampPalette(c("navy","royalblue","lightskyblue"))(200)                      
red <- colorRampPalette(c("mistyrose", "red2","darkred"))(200)

#plot
p <- ggplot(US, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = Percent),
               colour = alpha("white", 1/2), size = 0.05)  +
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  ggtitle("2012 US Election") +
  scale_fill_gradientn(colours=c(blue,"white", red), limits = c(100, -100))  +
  theme_void()

p <- ggplotly(p)

## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated

## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated

p

The streamgraph package in R

NOTE: These can be saved as static with legends

Example 1

#devtools::install_github("hrbrmstr/streamgraph")
suppressMessages(library(streamgraph))
 
# Create data:
year=rep(seq(1990,2016) , each=10)
name=rep(letters[1:10] , 27)
value=sample( seq(0,1,0.0001) , length(year))
data=data.frame(year, name, value)
 
# Graph 1: choose a RColorBrewer palette.
streamgraph(data, key="name", value="value", date="year")%>%
sg_fill_brewer("Blues")

Example 2 with interactive legends

suppressMessages(library(dplyr))

suppressMessages(library(viridis))

stocks_url <- "http://infographics.economist.com/2015/tech_stocks/data/stocks.csv"
stocks <- read.csv(stocks_url, stringsAsFactors=FALSE)

stock_colors <- viridis_pal()(100)
stocks %>% 
  mutate(date=as.Date(quarter, format="%m/%d/%y")) %>% 
  streamgraph(key="ticker", value="nominal", offset="expand") %>% 
  sg_fill_manual(stock_colors) %>% 
  sg_axis_x(tick_interval=10, tick_units="quarter") %>% 
  sg_legend(TRUE, "Ticker: ")

And now for something a “lil different”…

The gganimate package in R

Netherlands birth rate

birth<-read.csv("/Users/GB/Dropbox/Work_In_Progress/PERTUSSIS/Phylo-Project/Manuscript-FILES/BEAST/Covars/BirthRate/birth.csv", sep='', header=TRUE)
suppressMessages(library(ggplot2))
suppressMessages(library(gganimate))
pal <- c("#313695","#4575b4","#74add1","#abd9e9","#e0f3f8","#ffffbf","#fee090","#fdae61","#f46d43","#d73027","#a50026")
vals <- seq(10,32, length = 11)
#birth <- ggplot(birth, aes(x = Year, y = BirthRate, frame = Year, cumulative = TRUE)) +
  #geom_line(colour="black") +
  #geom_point(shape = 21, colour="black", aes(fill=BirthRate), size=5, stroke=1) +
  #scale_x_continuous(limits=c(1880,2015)) +
  #scale_y_continuous(limits=c(10,32)) +
  #theme_minimal() +
  #scale_fill_gradientn(colors = pal, values = vals, rescaler = function(x, ...) x, oob = identity, guide=FALSE) +
  #xlab("") +
  #ylab("Birth rate") +
  #theme(text=element_text(size=16, family="Georgia")))
#p<-gganimate(birth, "birth.mp4", ani.width = 750, ani.height = 500, interval = 0.1)

Recreating Gapminder

#p2 <- ggplot(gapminder_tween,
              #aes(x=x, y=y, frame = .frame)) +
   #geom_point(aes(size=population, color=continent),alpha=0.8) +
   #xlab("GDP per capita") +
   #ylab("Life expectancy at birth") 
#gganimate(p2, filename="gapminder.mp4", title_frame = FALSE, interval = 0.05)

You can also save it as a gif

#gganimate(p2, filename="gapminder.gif", title_frame = FALSE, interval = 0.05)

gif

Plotly version of Gapminder with dramatic pauses

suppressMessages(library(gapminder))
p <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
  geom_point(aes(size = pop, frame = year, ids = country)) +
  scale_x_log10()+theme_bw()

## Warning: Ignoring unknown aesthetics: frame, ids

p <- ggplotly(p)
p <- p %>% 
  animation_opts(
    1000, easing = "elastic", redraw = FALSE
  )
p

Extras

Column chart

Column <- gvisColumnChart(ptA, xvar="Year", 
                                    yvar=c("ptxA1", "ptxA2","ptxA4","ptxA5"))
print(Column,'chart')

Box and whiskers

Candle <- gvisCandlestickChart(OpenClose, 
                               options=list(legend='none'))
print(Candle,'chart')

Bubble chart

Bubble <- gvisBubbleChart(Fruits, idvar="Fruit",
                          xvar="Sales", yvar="Expenses",
                          colorvar="Year", sizevar="Profit",
                          options=list(
                            hAxis='{minValue:75, maxValue:125}', height=800, width=1000))
print(Bubble,'chart')

Sankey chart

datSK <- data.frame(From=c(rep("A",3), rep("B", 3)),
                To=c(rep(c("X", "Y", "Z"),2)),
                Weight=c(5,7,6,2,9,4))
colors_link <- c('pink', 'grey')
colors_link_array <- paste0("[", paste0("'", colors_link,"'", collapse = ','), "]")

colors_node <- c('yellow', 'lightblue', 'red', 'purple', 'green')
colors_node_array <- paste0("[", paste0("'", colors_node,"'", collapse = ','), "]")
opts <- paste0("{
        link: { colorMode: 'source',
                colors: ", colors_link_array ," },
        node: { colors: ", colors_node_array ," }
      }" )

Sankey<-gvisSankey(datSK, from="From", to="To", weight="Weight",
                     options=list(
                       sankey=opts))
print(Sankey,'chart')